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Fix Broken Link in Weibull Accelerated Failure Time Model Notebook #677

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Merged
merged 7 commits into from
Jul 29, 2024
Merged

Fix Broken Link in Weibull Accelerated Failure Time Model Notebook #677

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b1c7416
update link
pradyutf Jul 28, 2024
6a2069f
add ref attribute
pradyutf Jul 29, 2024
418aa73
use {ref} for links
pradyutf Jul 29, 2024
a920968
update links using {ref}
pradyutf Jul 29, 2024
d828b6b
Merge branch 'pymc-devs:main' into my-updates
pradyutf Jul 29, 2024
090a8b1
Run pre-commit and fix issues
pradyutf Jul 29, 2024
7568596
Merge branch 'my-updates' of https://github.com/pradyutf/pymc-example…
pradyutf Jul 29, 2024
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14 changes: 8 additions & 6 deletions examples/survival_analysis/bayes_param_survival_pymc3.ipynb
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2 changes: 2 additions & 0 deletions examples/survival_analysis/bayes_param_survival_pymc3.myst.md
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Expand Up @@ -10,6 +10,8 @@ kernelspec:
name: pymc3-dev
---

(bayes_param_survival_pymc3)=

# Bayesian Parametric Survival Analysis with PyMC3

```{code-cell} ipython3
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2 changes: 1 addition & 1 deletion examples/survival_analysis/weibull_aft.ipynb
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Expand Up @@ -57,7 +57,7 @@
"source": [
"## Dataset\n",
"\n",
"The [previous example notebook on Bayesian parametric survival analysis](https://docs.pymc.io/notebooks/bayes_param_survival.html) introduced two different accelerated failure time (AFT) models: Weibull and log-linear. In this notebook, we present three different parameterizations of the Weibull AFT model.\n",
"The {ref}`previous example notebook on Bayesian parametric survival analysis <bayes_param_survival_pymc3>` introduced two different accelerated failure time (AFT) models: Weibull and log-linear. In this notebook, we present three different parameterizations of the Weibull AFT model.\n",
"\n",
"The data set we'll use is the `flchain` R data set, which comes from a medical study investigating the effect of serum free light chain (FLC) on lifespan. Read the full documentation of the data by running:\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/survival_analysis/weibull_aft.myst.md
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Expand Up @@ -39,7 +39,7 @@ az.style.use("arviz-darkgrid")

## Dataset

The [previous example notebook on Bayesian parametric survival analysis](https://docs.pymc.io/notebooks/bayes_param_survival.html) introduced two different accelerated failure time (AFT) models: Weibull and log-linear. In this notebook, we present three different parameterizations of the Weibull AFT model.
The {ref}`previous example notebook on Bayesian parametric survival analysis <bayes_param_survival_pymc3>` introduced two different accelerated failure time (AFT) models: Weibull and log-linear. In this notebook, we present three different parameterizations of the Weibull AFT model.

The data set we'll use is the `flchain` R data set, which comes from a medical study investigating the effect of serum free light chain (FLC) on lifespan. Read the full documentation of the data by running:

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